Shannon's rate-distortion theory treats source symbols as unstructured labels. When the source is a knowledge base equipped with a logical proof system, a natural fidelity criterion is closure fidelity: a reconstruction is acceptable if it preserves the deductive closure of the original. This paper develops a rate-distortion theory under this criterion. Central to the theory is the irredundant core-a canonical generating set extracted by a fixed-order deletion procedure, from which the full deductive closure can be rederived. We prove that the zero-distortion semantic rate equals a quantity that is strictly below the classical entropy rate whenever the knowledge base contains redundant states. More generally, the full semantic rate-distortion function depends only on the core; redundant states are invisible to both rate and distortion. We derive a semantic source-channel separation theorem showing a semantic leverage phenomenon: under closure fidelity, the required source rate is reduced by an asymptotic leverage factor greater than one, allowing the same knowledge base to be communicated with proportionally fewer channel uses-not by violating Shannon capacity, but because redundant states become free. We also prove a strengthened Fano inequality that exploits core structure. For heterogeneous multi-agent communication, an overlap decomposition gives necessary and sufficient conditions for closure-reliable transmission and identifies a semantic bottleneck in broadcast settings that persists even over noiseless channels. All results are verified on Datalog instances with up to 24,000 base facts.
In wireless sensor networks (WSN), a large share of the energy demand arises from wireless communication, especially in wide area networks where transmission distances are at the scale of kilometers.Ensuring reliability of communication links while optimizing energy demand requires heterogeneous radio configurations throughout the network demanding for an automated process for identifying suitable transceiver settings in order to mitigate the effort of manual configuration during deployment.Furthermore, wireless links are susceptible to dynamic influences such as environmental conditions and interference from concurrent channel usage, rendering static radio configuration impractical.Therefore, autonomous organization and self-configuration of wireless communication networks, such as transmission parameter optimization, drastically reduce cost and effort for installation and maintenance of large-scale sensor systems.Such dynamic adaptive behavior can be achieved by local execution of decentralized methods that enable decision-making at the network edge, while also inherently offering advantages such as enhanced system robustness and scalability.In this work, we present a method that exemplifies this approach and experimentally evaluate its performance on real hardware.The adaptive algorithm optimizes the transmitter configuration of a LoRa transceiver by employing a model-free reinforcement learning approach based on an actor-critic setup using a parameterized stochastic policy and state-value function approximation.Experimental results show that the approach surpasses a standard approach in terms of long-term energy demand.Furthermore, the method's capability of adapting to dynamic wireless channels is demonstrated.
The future of communication technology is moving from 5G to 6G with new innovations. Blockchain (BC) is a such immersive technology that significantly impacts the betterment of communication technology. BC-based spectrum-sharing solutions can be used in Dynamic Spectrum Access (DSA) systems to fulfill the need for secure and efficient communication. With the invention of cognitive radio networks, DSA became a popular topic for the scientific community. Spectrum misuse/violations can occur due to the rapid growth of spectrum sharing. As the system is open to malicious attacks, licensed spectrum owners must be identified and verified. However, the existing BC-based DSA solutions are more expensive, non-optimized, and lack spectrum misuse detection. This paper proposes a novel consensus algorithm called “Proof of Equation” for spectrum misuse detection. The core of the proposed algorithm is a consensus score calculation based on a numerical equation with three parameters rather than using cryptographic calculations. The performance of the proposed algorithm is studied using Python simulations, and simulation results show that the proposed algorithm outperforms the Proof of Work (PoW) and Proof of Stake (PoS) consensus algorithms in terms of block production time.
We present an efficient simultaneous broadcast protocol ν-SimCast that allows n players to announce independently chosen values, even if up to t < n players are corrupt. Independence is guaranteed in the partially syn-2 chronous communication model, where communication is structured into rounds, while each round is asynchronous. The ν-SimCast protocol is more efficient than previous constructions. For repeated executions, we reduce the communication and computation complexity by a factor O(n). Combined with a deterministic extractor, ν-SimCast provides a particularly efficient solution for distributed coin-flipping. The protocol does not require any zero-knowledge proofs and is shown to be secure in the standard model under the Decisional Diffie Hellman assumption.